






"""simple docstring"""





import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


_SCREAMING_SNAKE_CASE   :							Dict          =		logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE   :							Optional[int]          =		tf.data.AUTOTUNE







def 	lowerCamelCase__			(				) ->							str:
  lowerCamelCase_       	=		argparse.ArgumentParser(description='Train a masked language model on TPU.'					)
  parser.add_argument(
      '--pretrained_model_config'						,							type=_lowerCamelCase						,							default='roberta-base'						,							help='The model config to use. Note that we don\'t copy the model\'s weights, only the config!'						,							)
  parser.add_argument(
      '--tokenizer'						,							type=_lowerCamelCase						,							default='unigram-tokenizer-wikitext'						,							help='The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'						,							)

  parser.add_argument(
      '--per_replica_batch_size'						,							type=_lowerCamelCase						,							default=8						,							help='Batch size per TPU core.'						,							)

  parser.add_argument(
      '--no_tpu'						,							action='store_true'						,							help='If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'						,							)

  parser.add_argument(
      '--tpu_name'						,							type=_lowerCamelCase						,							help='Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'						,							default='local'						,							)

  parser.add_argument(
      '--tpu_zone'						,							type=_lowerCamelCase						,							help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'						,							)

  parser.add_argument(
      '--gcp_project'						,							type=_lowerCamelCase						,							help='Google cloud project name. Only used for non-Colab TPU nodes.'					)

  parser.add_argument(
      '--bfloat16'						,							action='store_true'						,							help='Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'						,							)

  parser.add_argument(
      '--train_dataset'						,							type=_lowerCamelCase						,							help='Path to training dataset to load. If the path begins with `gs://`'
      ' then the dataset will be loaded from a Google Cloud Storage bucket.'						,							)

  parser.add_argument(
      '--shuffle_buffer_size'						,							type=_lowerCamelCase						,							default=2**18						,							help='Size of the shuffle buffer (in samples)'						,							)

  parser.add_argument(
      '--eval_dataset'						,							type=_lowerCamelCase						,							help='Path to evaluation dataset to load. If the path begins with `gs://`'
      ' then the dataset will be loaded from a Google Cloud Storage bucket.'						,							)

  parser.add_argument(
      '--num_epochs'						,							type=_lowerCamelCase						,							default=1						,							help='Number of epochs to train for.'						,							)

  parser.add_argument(
      '--learning_rate'						,							type=_lowerCamelCase						,							default=1e-4						,							help='Learning rate to use for training.'						,							)

  parser.add_argument(
      '--weight_decay_rate'						,							type=_lowerCamelCase						,							default=1e-3						,							help='Weight decay rate to use for training.'						,							)

  parser.add_argument(
      '--max_length'						,							type=_lowerCamelCase						,							default=512						,							help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'						,							)

  parser.add_argument(
      '--mlm_probability'						,							type=_lowerCamelCase						,							default=0.15						,							help='Fraction of tokens to mask during training.'						,							)

  parser.add_argument('--output_dir'						,							type=_lowerCamelCase						,							required=_lowerCamelCase						,							help='Path to save model checkpoints to.'					)
  parser.add_argument('--hub_model_id'						,							type=_lowerCamelCase						,							help='Model ID to upload to on the Hugging Face Hub.'					)

  lowerCamelCase_       	=		parser.parse_args()
  return args







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[Any]					) ->							Dict:
  try:
    if args.tpu_name:
      lowerCamelCase_       	=		tf.distribute.cluster_resolver.TPUClusterResolver(
          args.tpu_name						,							zone=args.tpu_zone						,							project=args.gcp_project					)
    else:
      lowerCamelCase_       	=		tf.distribute.cluster_resolver.TPUClusterResolver()
  except ValueError:
    raise RuntimeError(
        'Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '
        '--gcp_project. When running on a TPU VM, use --tpu_name local.'					)

  tf.config.experimental_connect_to_cluster(_lowerCamelCase					)
  tf.tpu.experimental.initialize_tpu_system(_lowerCamelCase					)

  return tpu







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any					) ->							Union[str, Any]:
  lowerCamelCase_       	=		0
  for file in file_list:
    lowerCamelCase_       	=		file.split('/'					)[-1]
    lowerCamelCase_       	=		re.search(r'-\d+-(\d+)\.tfrecord'						,							_lowerCamelCase					).group(1					)
    lowerCamelCase_       	=		int(_lowerCamelCase					)
    num_samples += sample_count

  return num_samples







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[Any]						,							_lowerCamelCase			:						List[str]						,							_lowerCamelCase			:						List[Any]						,							_lowerCamelCase			:						List[Any]						,							_lowerCamelCase			:						str						,							_lowerCamelCase			:						Any=None					) ->							Dict:
  lowerCamelCase_       	=		count_samples(_lowerCamelCase					)
  lowerCamelCase_       	=		tf.data.Dataset.from_tensor_slices(_lowerCamelCase					)
  if shuffle:
    lowerCamelCase_       	=		dataset.shuffle(len(_lowerCamelCase					)					)
  lowerCamelCase_       	=		tf.data.TFRecordDataset(_lowerCamelCase						,							num_parallel_reads=_lowerCamelCase					)
  # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
  lowerCamelCase_       	=		dataset.apply(tf.data.experimental.assert_cardinality(_lowerCamelCase					)					)
  lowerCamelCase_       	=		dataset.map(_lowerCamelCase						,							num_parallel_calls=_lowerCamelCase					)
  if shuffle:
    assert shuffle_buffer_size is not None
    lowerCamelCase_       	=		dataset.shuffle(args.shuffle_buffer_size					)
  lowerCamelCase_       	=		dataset.batch(_lowerCamelCase						,							drop_remainder=_lowerCamelCase					)
  lowerCamelCase_       	=		dataset.map(_lowerCamelCase						,							num_parallel_calls=_lowerCamelCase					)
  lowerCamelCase_       	=		dataset.prefetch(_lowerCamelCase					)
  return dataset







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict					) ->							str:
  if not args.no_tpu:
    lowerCamelCase_       	=		initialize_tpu(_lowerCamelCase					)
    lowerCamelCase_       	=		tf.distribute.TPUStrategy(_lowerCamelCase					)
  else:
    lowerCamelCase_       	=		tf.distribute.OneDeviceStrategy(device='/gpu:0'					)

  if args.bfloataa:
    tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'					)

  lowerCamelCase_       	=		AutoTokenizer.from_pretrained(args.tokenizer					)
  lowerCamelCase_       	=		AutoConfig.from_pretrained(args.pretrained_model_config					)
  lowerCamelCase_       	=		tokenizer.vocab_size

  lowerCamelCase_       	=		tf.io.gfile.glob(os.path.join(args.train_dataset						,							'*.tfrecord'					)					)
  if not training_records:
    raise ValueError(F'''No .tfrecord files found in {args.train_dataset}.'''					)
  lowerCamelCase_       	=		tf.io.gfile.glob(os.path.join(args.eval_dataset						,							'*.tfrecord'					)					)
  if not eval_records:
    raise ValueError(F'''No .tfrecord files found in {args.eval_dataset}.'''					)

  lowerCamelCase_       	=		count_samples(_lowerCamelCase					)

  lowerCamelCase_       	=		num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
  lowerCamelCase_       	=		steps_per_epoch * args.num_epochs

  with strategy.scope():
    lowerCamelCase_       	=		TFAutoModelForMaskedLM.from_config(_lowerCamelCase					)
    model(model.dummy_inputs					)  # Pass some dummy inputs through the model to ensure all the weights are built
    lowerCamelCase_	,      lowerCamelCase_       	=		create_optimizer(
        num_train_steps=_lowerCamelCase						,							num_warmup_steps=total_train_steps // 20						,							init_lr=args.learning_rate						,							weight_decay_rate=args.weight_decay_rate						,							)

    # Transformers models compute the right loss for their task by default when labels are passed, and will
    # use this for training unless you specify your own loss function in compile().
    model.compile(optimizer=_lowerCamelCase						,							metrics=['accuracy']					)

  def decode_fn(_lowerCamelCase			:						Optional[Any]					):
    lowerCamelCase_       	=		{
        'input_ids': tf.io.FixedLenFeature(dtype=tf.intaa						,							shape=(args.max_length,)					),
        'attention_mask': tf.io.FixedLenFeature(dtype=tf.intaa						,							shape=(args.max_length,)					),
    }
    return tf.io.parse_single_example(_lowerCamelCase						,							_lowerCamelCase					)

  # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
  # use their methods in our data pipeline.
  lowerCamelCase_       	=		DataCollatorForLanguageModeling(
      tokenizer=_lowerCamelCase						,							mlm_probability=args.mlm_probability						,							mlm=_lowerCamelCase						,							return_tensors='tf'					)

  def mask_with_collator(_lowerCamelCase			:						str					):
    # TF really needs an isin() function
    lowerCamelCase_       	=		(
        ~tf.cast(batch['attention_mask']						,							tf.bool					)
        | (batch['input_ids'] == tokenizer.cls_token_id)
        | (batch['input_ids'] == tokenizer.sep_token_id)
    )
    lowerCamelCase_	,      lowerCamelCase_       	=		data_collator.tf_mask_tokens(
        batch['input_ids']						,							vocab_size=len(_lowerCamelCase					)						,							mask_token_id=tokenizer.mask_token_id						,							special_tokens_mask=_lowerCamelCase						,							)
    return batch

  lowerCamelCase_       	=		args.per_replica_batch_size * strategy.num_replicas_in_sync

  lowerCamelCase_       	=		prepare_dataset(
      _lowerCamelCase						,							decode_fn=_lowerCamelCase						,							mask_fn=_lowerCamelCase						,							batch_size=_lowerCamelCase						,							shuffle=_lowerCamelCase						,							shuffle_buffer_size=args.shuffle_buffer_size						,							)

  lowerCamelCase_       	=		prepare_dataset(
      _lowerCamelCase						,							decode_fn=_lowerCamelCase						,							mask_fn=_lowerCamelCase						,							batch_size=_lowerCamelCase						,							shuffle=_lowerCamelCase						,							)

  lowerCamelCase_       	=		[]
  if args.hub_model_id:
    callbacks.append(
        PushToHubCallback(output_dir=args.output_dir						,							hub_model_id=args.hub_model_id						,							tokenizer=_lowerCamelCase					)					)

  model.fit(
      _lowerCamelCase						,							validation_data=_lowerCamelCase						,							epochs=args.num_epochs						,							callbacks=_lowerCamelCase						,							)

  model.save_pretrained(args.output_dir					)


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE   :							str          =		parse_args()
  main(args)












"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						int = 10						,							_lowerCamelCase			:						int = 1000						,							_lowerCamelCase			:						bool = True					) ->							int:
  assert (
      isinstance(_lowerCamelCase						,							_lowerCamelCase					)
      and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
      and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
  ), "Invalid type of value(s) specified to function!"

  if min_val > max_val:
    raise ValueError('Invalid value for min_val or max_val (min_value < max_value)'					)
  return min_val if option else max_val







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						int					) ->							int:
  return int((number_a + number_a) / 2					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						int						,							_lowerCamelCase			:						int					) ->							None:
  assert (
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and isinstance(_lowerCamelCase						,							_lowerCamelCase					) and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
  ), 'argument values must be type of "int"'

  if lower > higher:
    raise ValueError('argument value for lower and higher must be(lower > higher)'					)

  if not lower < to_guess < higher:
    raise ValueError(
        'guess value must be within the range of lower and higher value'					)

  def answer(_lowerCamelCase			:						int					) -> str:
    if number > to_guess:
      return "high"
    elif number < to_guess:
      return "low"
    else:
      return "same"

  print('started...'					)

  lowerCamelCase_       	=		lower
  lowerCamelCase_       	=		higher

  lowerCamelCase_       	=		[]

  while True:
    lowerCamelCase_       	=		get_avg(_lowerCamelCase						,							_lowerCamelCase					)
    last_numbers.append(_lowerCamelCase					)

    if answer(_lowerCamelCase					) == "low":
      lowerCamelCase_       	=		number
    elif answer(_lowerCamelCase					) == "high":
      lowerCamelCase_       	=		number
    else:
      break

  print(F'''guess the number : {last_numbers[-1]}'''					)
  print(F'''details : {last_numbers!s}'''					)







def 	lowerCamelCase__			(				) ->							None:
  lowerCamelCase_       	=		int(input('Enter lower value : '					).strip()					)
  lowerCamelCase_       	=		int(input('Enter high value : '					).strip()					)
  lowerCamelCase_       	=		int(input('Enter value to guess : '					).strip()					)
  guess_the_number(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)


if __name__ == "__main__":
  main()




